The project aims at developing and understand advanced chemical steel compositions and get insights of their behaviour during laser welding and laser cutting operations, to achieve higher energy efficiency of the processes along with higher robustness and to eliminate certain operations in the production chain. Six partners with complementary steel expertise will study three techniques. Laser-arc hybrid welding provides cooling cycles that are more suitable for advanced high strength steels compared to other techniques, provided the filler wire chemistry is matched. For its joint edge preparation, laser cutting is a candidate to further decrease energy and costs. However, when cutting certain advanced steels, uncontrolled melt oxidation can demand lower speed, which requires better understanding. For bi-metal laser welding of sawblades imperfections can arise that require significant rework by current practices, and therefore needs to be avoided. The proposed development will be accelerated by establishing a new, efficient test bed for faster tailoring and understanding of the steel chemistry in the respective thermal process. The recently accomplished experimental Snapshot testbed will be developed further, closely imitating the chosen processes. In particular it will be extended by a computational framework to predict the generated metallurgy, assisting physical experiments to adapt steel chemistry and microstructures for a particular application.
Tooling constitutes a significant part in the economical investment of the hot stamping process. Significant benefits in production economy and environmental benefits can be attained by improving the tribological performance in hot forming operations of automotive components. The main idea of this project is to create tailored tool surfaces on dies made from cheaper and easier to manufacture tool steel for the hot stamping of ultra-high strength steels.
SeeCut focuses on cost-efficient production systems for secure collection, analysis, visualization, storage and sharing of production data. The project addresses the integration of new signals and sensors. For advanced products, like jet engine components, collecting data during manufacturing and usage is essential for quality assurance and remanufacturing. The project goals are to identify key technical requirements related to production data for use in digitalized value chains, define means to ease the run-time integration of new signals and data streams from manufacturing machinery and sensors, and define how to securely and cost-efficiently share data through the value chains in circular production.
Every manufacturing company measure and control production performance with a system of KPIs. The aim of the SMART-PM project is to investigate and demonstrate new ways of collecting data, transforming data to information and introducing new decision tools based on valid information and economic models of the production systems.
Increased sustainability and cost effectiveness through improved strategic decision-making in production issues based on new metrics system for production and development.